
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "190"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 136 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 131 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.302758	7.362039	8.498764	29.926689	4.761123	84.490984	9.797418	46.477619	0.6519	0.6254	0.2039	13.380566	9.345729
2459593	digital_ok	100.00%	7.02%	0.00%	0.00%	100.00%	0.00%	41.158671	32.606976	7.055424	7.959107	19.628251	32.232387	22.091069	62.852685	0.5437	0.5844	0.2099	4.302896	4.182555
2459592	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.373488	-1.058045	0.127279	0.757918	-1.059339	0.906105	-1.937767	0.015089	0.0307	0.0323	0.0028	nan	nan
2459591	digital_ok	100.00%	13.51%	0.54%	0.00%	100.00%	0.00%	54.478736	42.640998	10.000903	9.271606	15.738869	22.490182	22.570188	59.788015	0.4821	0.5467	0.2885	2.621778	2.910611
2459590	digital_ok	100.00%	27.03%	34.05%	0.00%	100.00%	0.00%	57.737897	58.691697	9.160879	12.730075	25.614854	38.815015	38.966889	91.189759	0.4751	0.5030	0.2837	3.625499	3.459916
2459589	digital_ok	100.00%	23.23%	38.36%	0.00%	100.00%	0.00%	58.032076	59.251876	9.799003	14.244861	20.841790	28.896961	19.760236	23.013227	0.5156	0.4995	0.1510	3.934352	3.437394
2459588	digital_ok	100.00%	1.08%	35.12%	0.00%	100.00%	0.00%	32.164900	57.072959	9.121686	21.791330	18.712472	28.686446	16.257989	20.249040	0.5721	0.5117	0.1566	4.260539	3.102173
2459587	digital_ok	100.00%	-	-	-	100.00%	0.00%	52.455943	82.245392	13.577435	29.764388	2.297025	4.250505	23.539934	34.853052	nan	nan	nan	0.000000	0.000000
2459586	digital_ok	100.00%	22.16%	48.65%	0.00%	100.00%	0.00%	34.856755	67.592542	1.339294	4.605700	8.018748	13.282831	34.181506	46.057688	0.5083	0.4608	0.2280	4.129361	4.727864
2459585	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.803704	-1.118183	-0.759810	0.620036	-0.807122	0.208616	-1.241245	0.253329	0.0325	0.0322	0.0009	0.000000	0.000000
2459584	digital_ok	100.00%	40.22%	25.81%	0.00%	100.00%	0.00%	69.063454	70.759268	9.933308	11.238188	18.606363	10.605186	15.356868	52.229619	0.4383	0.4652	0.2318	8.714254	5.623268
2459583	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459582	digital_ok	100.00%	56.22%	50.81%	0.00%	100.00%	0.00%	49.165555	68.104592	3.093306	5.284005	27.572303	28.404318	28.085346	52.550261	0.4620	0.4484	0.2185	2.968056	3.297037
2459581	digital_ok	100.00%	49.19%	51.89%	0.00%	100.00%	0.00%	70.795969	88.586983	3.764782	5.363370	14.536846	26.020011	24.045043	83.795865	0.4456	0.4602	0.2064	2.426530	2.094179
2459580	digital_ok	100.00%	36.74%	17.29%	0.00%	100.00%	0.00%	53.679915	53.779890	3.983248	3.878838	15.230097	18.902324	45.629881	99.438416	0.4538	0.5031	0.2457	3.815931	4.595274
2459579	digital_ok	100.00%	28.63%	57.27%	0.00%	100.00%	0.00%	50.049769	74.534006	3.821976	7.819222	27.781405	18.797476	24.124659	11.521420	0.4671	0.4352	0.2150	3.518032	3.036731
2459578	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.099758	-0.049468	-0.729732	-0.737691	-0.069726	-0.411844	-0.612961	0.015700	0.0335	0.0341	0.0012	nan	nan
2459577	digital_ok	100.00%	25.97%	31.93%	0.00%	100.00%	0.00%	62.704705	70.029190	2.708216	3.881708	22.232431	21.008783	31.858882	108.610842	0.4668	0.4752	0.2028	3.473617	3.572570
2459576	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459575	digital_ok	100.00%	7.02%	8.64%	0.00%	100.00%	0.00%	46.886172	60.100170	2.079590	3.800897	10.275542	14.236007	4.516415	5.907815	0.5972	0.5774	0.1871	2.714267	2.395649
2459574	digital_ok	100.00%	23.81%	49.78%	0.00%	100.00%	0.00%	41.326208	65.029592	1.665838	4.642563	9.393395	18.356096	16.779002	36.292852	0.5007	0.4566	0.2178	3.421337	2.995380
2459573	digital_ok	100.00%	14.60%	36.24%	0.00%	100.00%	0.00%	47.515539	66.994442	3.879054	7.085654	25.998718	27.649009	13.851522	30.246570	0.4796	0.4713	0.2173	8.418577	4.468821
2459572	digital_ok	100.00%	4.32%	31.89%	0.00%	100.00%	0.00%	44.842758	69.515248	1.813270	4.824385	6.687177	9.844895	4.583905	12.080186	0.5065	0.4758	0.2161	4.249109	4.130334
2459571	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.080927	-0.090863	-0.678491	-0.767899	-0.793542	0.021906	-0.743622	0.019781	0.0355	0.0378	0.0024	nan	nan
2459570	digital_ok	100.00%	25.41%	49.73%	0.00%	100.00%	0.00%	47.729819	85.007919	9.647538	14.472277	2.390790	2.424563	14.582806	31.207754	0.5230	0.4949	0.2327	0.000000	0.000000
2459569	digital_ok	100.00%	6.49%	22.73%	0.00%	100.00%	0.00%	36.342382	50.211282	1.590264	3.725829	9.397407	11.592114	8.457136	15.323210	0.6603	0.6299	0.1594	7.462619	6.654025
2459566	digital_ok	100.00%	2.16%	8.65%	0.00%	100.00%	0.00%	42.421613	42.260901	2.364919	2.853964	12.010175	18.481325	19.725970	32.272860	0.5204	0.5472	0.2864	0.000000	0.000000
2459565	digital_ok	0.00%	-	-	-	93.33%	0.00%	1.074879	1.074879	0.972728	0.972728	0.664032	0.664032	0.146470	0.146470	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	7.799568	34.300243	0.977240	3.079046	1.605681	-0.996026	18.957458	21.645024	0.6524	0.5871	0.3838	4.569439	4.685107
2459563	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	13.853716	37.372817	2.477278	5.085869	9.359927	8.134918	27.660606	14.560019	0.6338	0.5881	0.3662	2.620464	2.232928
2459562	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	13.256340	21.798418	-0.074630	1.194398	4.907003	3.039137	26.869491	15.109590	0.6164	0.6105	0.3584	4.488699	4.311161
2459561	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	8.059822	27.745718	-0.365885	1.187311	1.547144	-1.115956	6.132674	4.586048	0.6356	0.5923	0.3523	4.293958	3.178742
2459560	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	11.010512	26.381914	0.104785	1.235008	-0.469087	-0.445072	7.819646	7.403806	0.6252	0.5810	0.3373	5.611675	4.858711
2459559	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	8.030217	31.341021	-0.362791	1.422249	-0.113634	-0.098076	17.705192	9.702952	0.6448	0.5842	0.3607	4.260682	4.963431
2459558	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	8.065226	25.513993	0.025184	1.242383	1.968967	13.735494	11.356742	77.196938	0.6544	0.6237	0.3886	7.331025	6.220409
2459557	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0313	0.0322	0.0012	nan	nan
2459556	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	8.940772	17.975002	-0.071977	1.089533	2.640239	12.876541	29.075265	156.683889	0.6396	0.6298	0.4014	7.364327	6.616494
2459554	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6467	0.6120	0.3846	nan	nan
2459553	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6474	0.6078	0.3824	nan	nan
2459552	digital_ok	100.00%	1.62%	0.00%	0.00%	100.00%	0.00%	53.117678	45.009133	3.853172	3.435446	13.702906	17.076848	17.055957	57.291219	0.5333	0.5785	0.2741	1.830228	1.721080
2459551	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	28.486016	22.918928	1.192942	1.388675	-0.674959	-0.415740	8.702824	17.330061	0.5286	0.5952	0.2808	5.054237	5.298479
2459550	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0307	0.0323	0.0015	nan	nan
2459549	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	41.330050	34.913116	2.730830	2.450848	9.284872	6.962073	23.010109	15.981528	0.5266	0.5899	0.2787	3.943398	4.350835
2459542	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	53.038199	40.492220	6.559781	5.355983	0.344415	0.183494	20.264047	34.254996	0.5951	0.6724	0.2615	4.168802	5.029079
2459541	digital_ok	100.00%	1.34%	1.79%	0.00%	100.00%	0.00%	46.280318	37.877291	3.097026	3.516483	4.630423	5.305697	12.909569	10.294816	0.5953	0.6479	0.2392	2.157212	2.215270
2459540	digital_ok	100.00%	0.45%	0.00%	0.00%	100.00%	0.00%	33.591676	27.704426	0.330260	0.497314	8.412147	1.586323	27.638246	16.855536	0.6234	0.6581	0.2847	3.554497	3.857576
2459536	digital_ok	100.00%	5.93%	0.00%	0.00%	100.00%	0.00%	73.756010	50.212875	5.565429	3.422159	13.093836	12.753122	34.093090	37.096246	0.5455	0.6078	0.3248	0.000000	0.000000
2459535	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	21.819912	21.413497	0.275191	0.543936	10.017167	1.918929	45.071812	35.124887	0.6587	0.6877	0.3159	1.012566	1.023000
2459534	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	18.039583	25.391310	-0.142229	0.344117	8.870063	0.424580	62.242331	11.616784	0.6807	0.6597	0.3201	0.850991	0.852012
2459533	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459532	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	43.420633	44.876298	2.347235	2.870036	32.538631	10.508138	50.115836	28.052023	0.5862	0.6184	0.3425	1.685966	1.531727
2459530	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.193257	27.313471	-0.523877	0.751379	0.116468	-0.444490	20.455391	8.877824	0.6867	0.6196	0.3985	0.873731	0.871280
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459523	digital_ok	100.00%	-	-	-	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459522	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459513	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.262629	-0.005829	-0.724152	-0.511116	0.427709	0.125097	-0.818246	-0.325052	0.0363	0.0392	0.0018	nan	nan
2459508	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.645524	16.576915	-0.464052	2.206870	1.136632	23.027488	1.658534	55.522976	0.9025	0.9407	0.1687	0.000000	0.000000
2459505	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.828529	45.865904	-0.227076	5.266897	1.833624	4.416179	12.740203	16.806118	0.8856	0.7683	0.2227	27.733945	29.812517
2459503	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	8.053666	23.140290	0.052666	5.651053	5.168803	10.922107	131.058267	194.903293	0.0645	0.0792	0.0060	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 190 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 190, Node 15:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 190: 2459663

Antenna 190: 2459662

Antenna 190: 2459661

Antenna 190: 2459660

Antenna 190: 2459659

Antenna 190: 2459658

Antenna 190: 2459657

Antenna 190: 2459656

Antenna 190: 2459655

Antenna 190: 2459653

Antenna 190: 2459652

Antenna 190: 2459651

Antenna 190: 2459650

Antenna 190: 2459649

Antenna 190: 2459648

Antenna 190: 2459647

Antenna 190: 2459646

Antenna 190: 2459645

Antenna 190: 2459642

Antenna 190: 2459640

Antenna 190: 2459639

Antenna 190: 2459638

Antenna 190: 2459637

Antenna 190: 2459636

Antenna 190: 2459635

Antenna 190: 2459634

Antenna 190: 2459633

Antenna 190: 2459632

Antenna 190: 2459631

Antenna 190: 2459630

Antenna 190: 2459629

Antenna 190: 2459628

Antenna 190: 2459627

Antenna 190: 2459626

Antenna 190: 2459625

Antenna 190: 2459624

Antenna 190: 2459623

Antenna 190: 2459622

Antenna 190: 2459621

Antenna 190: 2459620

Antenna 190: 2459619

Antenna 190: 2459618

Antenna 190: 2459617

Antenna 190: 2459616

Antenna 190: 2459615

Antenna 190: 2459614

Antenna 190: 2459613

Antenna 190: 2459612

Antenna 190: 2459611

Antenna 190: 2459610

Antenna 190: 2459609

Antenna 190: 2459608

Antenna 190: 2459607

Antenna 190: 2459605

Antenna 190: 2459604

Antenna 190: 2459603

Antenna 190: 2459602

Antenna 190: 2459598

Antenna 190: 2459597

Antenna 190: 2459596

Antenna 190: 2459595

Antenna 190: 2459594

Antenna 190: 2459593

Antenna 190: 2459592

Antenna 190: 2459591

Antenna 190: 2459590

Antenna 190: 2459589

Antenna 190: 2459588

Antenna 190: 2459587

Antenna 190: 2459586

Antenna 190: 2459585

Antenna 190: 2459584

Antenna 190: 2459583

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶